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## Uses of Class
org.metaqtl.EMResult

Packages that use EMResult
org.metaqtl |
org.metaqtl.adapter |
org.metaqtl.algo |
org.metaqtl.factory |

Uses of EMResult in org.metaqtl

Fields in org.metaqtl declared as EMResult
`EMResult[]` | `MetaQtlResult.clusterings` The meta qtl clusterings.

Methods in org.metaqtl that return EMResult
`EMResult` | `MetaQtlResult.getBestClustering(int criterion)` This methods returns the best models for each criterion defined to select the optimal number of clusters per trait.
`static EMResult` | `EMCriteria.getBestResult(EMResult[] clusterings, int criterion)` For the given criterion `criterion` this methods looks into the array of `EMResult` and find the model which is optimal.
`static EMResult` | `EMCriteria.getBestResult(EMResult[] clusterings, int n, int criterion)`
`static EMResult` | `EMCriteria.getBestResult(EMResult[] clusterings, int n, java.lang.String criterion)`
`static EMResult` | `EMCriteria.getBestResult(EMResult[] clusterings, java.lang.String criterion)`
`EMResult` | `MetaQtlResult.getClustering(int k)` Returns the Qtl clustering result with `k` clusters.

Methods in org.metaqtl with parameters of type EMResult
`static void` | `EMResult.copy(EMResult dest, EMResult src)`
`static EMResult` | `EMCriteria.getBestResult(EMResult[] clusterings, int criterion)` For the given criterion `criterion` this methods looks into the array of `EMResult` and find the model which is optimal.
`static EMResult` | `EMCriteria.getBestResult(EMResult[] clusterings, int n, int criterion)`
`static EMResult` | `EMCriteria.getBestResult(EMResult[] clusterings, int n, java.lang.String criterion)`
`static EMResult` | `EMCriteria.getBestResult(EMResult[] clusterings, java.lang.String criterion)`
`double[]` | `MetaQtlResult.getUSD(EMResult best, int kmin, int kmax, java.lang.String criterion)`
`void` | `MetaQtlResult.setClusterings(EMResult[] results)`

Uses of EMResult in org.metaqtl.adapter

Methods in org.metaqtl.adapter with parameters of type EMResult
`static Qtl[]` | `QtlAdapter.adapt(EMResult result)` From a result of a EM clustering returns the mixture components as an array of Qtl.

Uses of EMResult in org.metaqtl.algo

Methods in org.metaqtl.algo that return EMResult
`static EMResult` | `EMAlgorithm.doEM(double[] x, double[] sd, int k, EMResult spoint)` Apply the EM-Algorithm on the given data set where `x` is an array of observed value and `sd` an array of same size than `x` which stores the standard deviations of the observed values.

Methods in org.metaqtl.algo with parameters of type EMResult
`static void` | `EMAlgorithm.computeCOV(double[] sd, EMResult theta)` Computes the variance-covariance matrix of the mixture component estimates.
`static void` | `EMAlgorithm.computeDM(double[] x, double[] sd, double[] mu, EMResult theta)` Computes the matrix of derivatives of the update function.
`static EMResult` | `EMAlgorithm.doEM(double[] x, double[] sd, int k, EMResult spoint)` Apply the EM-Algorithm on the given data set where `x` is an array of observed value and `sd` an array of same size than `x` which stores the standard deviations of the observed values.
`static void` | `EMAlgorithm.emRate(EMResult theta, double[] mu, double[] pi)` Compute the euclidean distance between the new parameters and the old ones to obtain a simple approximation of the EM convergence rate.
`static void` | `EMAlgorithm.eStep(double[] x, double[] sd, EMResult theta)` The Expectation step : it just consists in updating the Z matrix, i.e the cluster membership probabilities.
`static void` | `EMAlgorithm.initEM(double[] x, double[] sd, EMResult theta, java.util.Random rng)` Initializes the EM algorithm by randomly assigning observations to the clusters and do one M-Step to compute the first values of the parameters.
`static int` | `EMAlgorithm.iterate(double[] x, double[] sd, EMResult theta)` This method performs one iteration of the EM-algorithm.
`static int` | `EMAlgorithm.mStep(double[] x, double[] sd, EMResult theta, double err)` The Maximization step : here this step is straighforward and simple analytical formula are applied to obtain the new parameter estimates.
`static void` | `EMAlgorithm.updateLoglikelihood(double[] x, double[] sd, EMResult theta)` Updates the loglikelihood value.
`static double[]` | `EMAlgorithm.updateMuVector(double[] x, double[] sd, EMResult theta)` Updates the vector of mixture components and returns the new values.
`static double[]` | `EMAlgorithm.updatePiVector(EMResult theta)` Updates the vector of mixture mixings and returns the new values.
`static void` | `EMAlgorithm.updateZMatrix(double[] x, double[] sd, EMResult theta)` Updates the Z matrix.

Uses of EMResult in org.metaqtl.factory

Methods in org.metaqtl.factory that return EMResult
`static EMResult` | `EMResultFactory.read(java.io.BufferedReader buffer)`

Methods in org.metaqtl.factory with parameters of type EMResult
`static void` | `EMResultFactory.write(EMResult result, java.io.OutputStream stream)`
`static void` | `EMResultFactory.write(EMResult result, java.io.PrintWriter writer)`

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